import math
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

# batch_size = 32
# num_steps = 35
# train_iter,vocab = d2l.d2l.load_data_time_machine(batch_size,num_steps)
#
# # one hot
#
# def get_params(vocab_size,num_hiddens,device):
#     num_inputs = num_outputs = vocab_size
#
#     def normal(shape):
#         return torch.randn(size=shape,device=device) * 0.01
#
#     W_xh = normal((num_inputs,num_hiddens))
#     W_hh = normal((num_hiddens,num_hiddens))
#     b_h = torch.zeros(num_hiddens,device=device)
#     W_hq = normal((num_hiddens,num_outputs))
#     b_q = torch.zeros(num_outputs,device=device)
#     params = [W_xh,W_hh,b_h,W_hq,b_q]
#     for param in params:
#         param.requires_grad_(True)
#     return params
#
# def init_rnn_state(batch_size, num_hiddens, device):
#     return (torch.zeros((batch_size, num_hiddens), device=device), )
#
# def rnn(inputs, state, params):
#     # inputs的形状：(时间步数量，批量大小，词表大小)
#     W_xh, W_hh, b_h, W_hq, b_q = params
#     H, = state
#     outputs = []
#     # X的形状：(批量大小，词表大小)
#     for X in inputs:
#         H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h)
#         Y = torch.mm(H, W_hq) + b_q
#         outputs.append(Y)
#     return torch.cat(outputs, dim=0), (H,)
#
# class RNNModelScratch: #@save
#     """从零开始实现的循环神经网络模型"""
#     def __init__(self, vocab_size, num_hiddens, device,
#                  get_params, init_state, forward_fn):
#         self.vocab_size, self.num_hiddens = vocab_size, num_hiddens
#         self.params = get_params(vocab_size, num_hiddens, device)
#         self.init_state, self.forward_fn = init_state, forward_fn
#
#     def __call__(self, X, state):
#         X = F.one_hot(X.T, self.vocab_size).type(torch.float32)
#         return self.forward_fn(X, state, self.params)
#
#     def begin_state(self, batch_size, device):
#         return self.init_state(batch_size, self.num_hiddens, device)
#
# num_hiddens = 512
# net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), get_params,
#                       init_rnn_state, rnn)
#
# def predict_ch8(prefix, num_preds, net, vocab, device):  #@save
#     """在prefix后面生成新字符"""
#     state = net.begin_state(batch_size=1, device=device)
#     outputs = [vocab[prefix[0]]]
#     get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape((1, 1))
#     for y in prefix[1:]:  # 预热期
#         _, state = net(get_input(), state)
#         outputs.append(vocab[y])
#     for _ in range(num_preds):  # 预测num_preds步
#         y, state = net(get_input(), state)
#         outputs.append(int(y.argmax(dim=1).reshape(1)))
#     return ''.join([vocab.idx_to_token[i] for i in outputs])
#
# def grad_clipping(net,theta):
#     if isinstance(net,nn.Module):
#         params = [p for p in net.parameters() if p.requires_grad]
#     else:
#         params = net.params
#     norm = torch.sqrt(sum(torch.sum((p.grad**2))for p in params))
#     if norm>theta:
#         for param in params:
#             param.grad[:] *= theta/norm
#
# def train_epoch_ch8(net,train_iter,loss,updater,device,use_random_iter):
#     state,timer = None,d2l.Timer()
#     metric = d2l.Accumulator(2)
#     for X,Y in train_iter:
#         if state is None or use_random_iter:
#             state = net.begin_state(batch_size=X.shape[0],device=device)
#         else:
#             if isinstance(net, nn.Module) and not isinstance(state, tuple):
#                 # state对于nn.GRU是个张量
#                 state.detach_()
#             else:
#                 # state对于nn.LSTM或对于我们从零开始实现的模型是个张量
#                 for s in state:
#                     s.detach_()
#         y = Y.T.reshape(-1)
#         X,y = X.to(device),y.to(device)
#         y_hat,state = net(X,state)
#         l = loss(y_hat,y.long()).mean()
#         if isinstance(updater, torch.optim.Optimizer):
#             updater.zero_grad()
#             l.backward()
#             grad_clipping(net, 1)
#             updater.step()
#         else:
#             l.backward()
#             grad_clipping(net, 1)
#             # 因为已经调用了mean函数
#             updater(batch_size=1)
#         metric.add(l * y.numel(), y.numel())
#     return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()
#
# #@save
# def train_ch8(net, train_iter, vocab, lr, num_epochs, device,
#               use_random_iter=False):
#     """训练模型（定义见第8章）"""
#     loss = nn.CrossEntropyLoss()
#     animator = d2l.Animator(xlabel='epoch', ylabel='perplexity',
#                             legend=['train'], xlim=[10, num_epochs])
#     # 初始化
#     if isinstance(net, nn.Module):
#         updater = torch.optim.SGD(net.parameters(), lr)
#     else:
#         updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)
#     predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)
#     # 训练和预测
#     for epoch in range(num_epochs):
#         ppl, speed = train_epoch_ch8(
#             net, train_iter, loss, updater, device, use_random_iter)
#         if (epoch + 1) % 10 == 0:
#             print(predict('time traveller'))
#             animator.add(epoch + 1, [ppl])
#     print(f'困惑度 {ppl:.1f}, {speed:.1f} 词元/秒 {str(device)}')
#     print(predict('time traveller'))
#     print(predict('traveller'))
#
# num_epochs, lr = 500, 1
# train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu())
# d2l.plt.show()
# train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu(),use_random_iter=True)
# d2l.plt.show()

batch_size,num_steps = 32,35
train_iter,vocab = d2l.load_data_time_machine(batch_size,num_steps)

def get_params(vocab_size, num_hiddens, device):
    num_inputs = num_outputs = vocab_size

    def normal(shape):
        return torch.randn(size=shape, device=device)*0.01

    def three():
        return (normal((num_inputs, num_hiddens)),
                normal((num_hiddens, num_hiddens)),
                torch.zeros(num_hiddens, device=device))

    W_xz, W_hz, b_z = three()  # 更新门参数
    W_xr, W_hr, b_r = three()  # 重置门参数
    W_xh, W_hh, b_h = three()  # 候选隐状态参数
    # 输出层参数
    W_hq = normal((num_hiddens, num_outputs))
    b_q = torch.zeros(num_outputs, device=device)
    # 附加梯度
    params = [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]
    for param in params:
        param.requires_grad_(True)
    return params

def init_gru_state(batch_size, num_hiddens, device):
    return (torch.zeros((batch_size, num_hiddens), device=device), )

def gru(inputs, state, params):
    W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state
    outputs = []
    for X in inputs:
        Z = torch.sigmoid((X @ W_xz) + (H @ W_hz) + b_z)
        R = torch.sigmoid((X @ W_xr) + (H @ W_hr) + b_r)
        H_tilda = torch.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = H @ W_hq + b_q
        outputs.append(Y)
    return torch.cat(outputs, dim=0), (H,)

vocab_size, num_hiddens, device = len(vocab), 256, d2l.try_gpu()
num_epochs, lr = 500, 1
model = d2l.RNNModelScratch(len(vocab), num_hiddens, device, get_params,
                            init_gru_state, gru)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)






























